def test_dnn(self): with tf.Graph().as_default(): X = [3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1] Y = [1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3] input = zqtflearn.input_data(shape=[None]) linear = zqtflearn.single_unit(input) regression = zqtflearn.regression(linear, optimizer='sgd', loss='mean_square', metric='R2', learning_rate=0.01) m = zqtflearn.DNN(regression) # Testing fit and predict m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False) res = m.predict([3.2])[0] self.assertGreater(res, 1.3, "DNN test (linear regression) failed! with score: " + str(res) + " expected > 1.3") self.assertLess(res, 1.8, "DNN test (linear regression) failed! with score: " + str(res) + " expected < 1.8") # Testing save method m.save("test_dnn.zqtflearn") self.assertTrue(os.path.exists("test_dnn.zqtflearn.index")) with tf.Graph().as_default(): input = zqtflearn.input_data(shape=[None]) linear = zqtflearn.single_unit(input) regression = zqtflearn.regression(linear, optimizer='sgd', loss='mean_square', metric='R2', learning_rate=0.01) m = zqtflearn.DNN(regression) # Testing load method m.load("test_dnn.zqtflearn") res = m.predict([3.2])[0] self.assertGreater(res, 1.3, "DNN test (linear regression) failed after loading model! score: " + str(res) + " expected > 1.3") self.assertLess(res, 1.8, "DNN test (linear regression) failed after loading model! score: " + str(res) + " expected < 1.8")
def test_conv_layers(self): X = [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 1., 0.], [1., 1., 1., 0.]] Y = [[1., 0.], [0., 1.], [1., 0.], [0., 1.]] with tf.Graph().as_default(): g = zqtflearn.input_data(shape=[None, 4]) g = zqtflearn.reshape(g, new_shape=[-1, 2, 2, 1]) g = zqtflearn.conv_2d(g, 4, 2, activation='relu') g = zqtflearn.max_pool_2d(g, 2) g = zqtflearn.fully_connected(g, 2, activation='softmax') g = zqtflearn.regression(g, optimizer='sgd', learning_rate=1.) m = zqtflearn.DNN(g) m.fit(X, Y, n_epoch=100, snapshot_epoch=False) # TODO: Fix test #self.assertGreater(m.predict([[1., 0., 0., 0.]])[0][0], 0.5) # Bulk Tests with tf.Graph().as_default(): g = zqtflearn.input_data(shape=[None, 4]) g = zqtflearn.reshape(g, new_shape=[-1, 2, 2, 1]) g = zqtflearn.conv_2d(g, 4, 2) g = zqtflearn.conv_2d(g, 4, 1) g = zqtflearn.conv_2d_transpose(g, 4, 2, [2, 2]) g = zqtflearn.max_pool_2d(g, 2)
def __init__(self): network = zqtflearn.input_data(shape=[None, 784], name="input") network = self.make_core_network(network) network = regression(network, optimizer='adam', learning_rate=0.01, loss='categorical_crossentropy', name='target') model = zqtflearn.DNN(network, tensorboard_verbose=0) self.model = model
def test_vbs1(self): with tf.Graph().as_default(): # Data loading and preprocessing import zqtflearn.datasets.mnist as mnist X, Y, testX, testY = mnist.load_data(one_hot=True) X = X.reshape([-1, 28, 28, 1]) testX = testX.reshape([-1, 28, 28, 1]) X = X[:20, :, :, :] Y = Y[:20, :] testX = testX[:10, :, :, :] testY = testY[:10, :] # Building convolutional network network = input_data(shape=[None, 28, 28, 1], name='input') network = conv_2d(network, 32, 3, activation='relu', regularizer="L2") network = max_pool_2d(network, 2) network = local_response_normalization(network) network = conv_2d(network, 64, 3, activation='relu', regularizer="L2") network = max_pool_2d(network, 2) network = local_response_normalization(network) network = fully_connected(network, 128, activation='tanh') network = dropout(network, 0.8) network = fully_connected(network, 256, activation='tanh') network = dropout(network, 0.8) network = fully_connected(network, 10, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.01, loss='categorical_crossentropy', name='target') # Training model = zqtflearn.DNN(network, tensorboard_verbose=3) model.fit({'input': X}, {'target': Y}, n_epoch=1, batch_size=10, validation_set=({ 'input': testX }, { 'target': testY }), validation_batch_size=5, snapshot_step=10, show_metric=True, run_id='convnet_mnist_vbs') self.assertEqual(model.train_ops[0].validation_batch_size, 5) self.assertEqual(model.train_ops[0].batch_size, 10)
def __init__(self): # Building deep neural network network = zqtflearn.input_data(shape=[None, 784], name="input") network = self.make_core_network(network) # Regression using SGD with learning rate decay and Top-3 accuracy sgd = zqtflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=1000) top_k = zqtflearn.metrics.Top_k(3) network = zqtflearn.regression(network, optimizer=sgd, metric=top_k, loss='categorical_crossentropy', name="target") model = zqtflearn.DNN(network, tensorboard_verbose=0) self.model = model
def __init__(self): inputs = zqtflearn.input_data(shape=[None, 784], name="input") with tf.variable_scope("scope1") as scope: net_conv = Model1.make_core_network(inputs) # shape (?, 10) with tf.variable_scope("scope2") as scope: net_dnn = Model2.make_core_network(inputs) # shape (?, 10) network = tf.concat([net_conv, net_dnn], 1, name="concat") # shape (?, 20) network = zqtflearn.fully_connected(network, 10, activation="softmax") network = regression(network, optimizer='adam', learning_rate=0.01, loss='categorical_crossentropy', name='target') self.model = zqtflearn.DNN(network, tensorboard_verbose=0)
def test_recurrent_layers(self): X = [[1, 3, 5, 7], [2, 4, 8, 10], [1, 5, 9, 11], [2, 6, 8, 0]] Y = [[0., 1.], [1., 0.], [0., 1.], [1., 0.]] with tf.Graph().as_default(): g = zqtflearn.input_data(shape=[None, 4]) g = zqtflearn.embedding(g, input_dim=12, output_dim=4) g = zqtflearn.lstm(g, 6) g = zqtflearn.fully_connected(g, 2, activation='softmax') g = zqtflearn.regression(g, optimizer='sgd', learning_rate=1.) m = zqtflearn.DNN(g) m.fit(X, Y, n_epoch=300, snapshot_epoch=False) self.assertGreater(m.predict([[5, 9, 11, 1]])[0][1], 0.9)
def test_core_layers(self): X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]] Y_nand = [[1.], [1.], [1.], [0.]] Y_or = [[0.], [1.], [1.], [1.]] # Graph definition with tf.Graph().as_default(): # Building a network with 2 optimizers g = zqtflearn.input_data(shape=[None, 2]) # Nand operator definition g_nand = zqtflearn.fully_connected(g, 32, activation='linear') g_nand = zqtflearn.fully_connected(g_nand, 32, activation='linear') g_nand = zqtflearn.fully_connected(g_nand, 1, activation='sigmoid') g_nand = zqtflearn.regression(g_nand, optimizer='sgd', learning_rate=2., loss='binary_crossentropy') # Or operator definition g_or = zqtflearn.fully_connected(g, 32, activation='linear') g_or = zqtflearn.fully_connected(g_or, 32, activation='linear') g_or = zqtflearn.fully_connected(g_or, 1, activation='sigmoid') g_or = zqtflearn.regression(g_or, optimizer='sgd', learning_rate=2., loss='binary_crossentropy') # XOR merging Nand and Or operators g_xor = zqtflearn.merge([g_nand, g_or], mode='elemwise_mul') # Training m = zqtflearn.DNN(g_xor) m.fit(X, [Y_nand, Y_or], n_epoch=400, snapshot_epoch=False) # Testing self.assertLess(m.predict([[0., 0.]])[0][0], 0.01) self.assertGreater(m.predict([[0., 1.]])[0][0], 0.9) self.assertGreater(m.predict([[1., 0.]])[0][0], 0.9) self.assertLess(m.predict([[1., 1.]])[0][0], 0.01) # Bulk Tests with tf.Graph().as_default(): net = zqtflearn.input_data(shape=[None, 2]) net = zqtflearn.flatten(net) net = zqtflearn.reshape(net, new_shape=[-1]) net = zqtflearn.activation(net, 'relu') net = zqtflearn.dropout(net, 0.5) net = zqtflearn.single_unit(net)
def test_feed_dict_no_None(self): X = [[0., 0., 0., 0.], [1., 1., 1., 1.], [0., 0., 1., 0.], [1., 1., 1., 0.]] Y = [[1., 0.], [0., 1.], [1., 0.], [0., 1.]] with tf.Graph().as_default(): g = zqtflearn.input_data(shape=[None, 4], name="X_in") g = zqtflearn.reshape(g, new_shape=[-1, 2, 2, 1]) g = zqtflearn.conv_2d(g, 4, 2) g = zqtflearn.conv_2d(g, 4, 1) g = zqtflearn.max_pool_2d(g, 2) g = zqtflearn.fully_connected(g, 2, activation='softmax') g = zqtflearn.regression(g, optimizer='sgd', learning_rate=1.) m = zqtflearn.DNN(g) def do_fit(): m.fit({"X_in": X, 'non_existent': X}, Y, n_epoch=30, snapshot_epoch=False) self.assertRaisesRegexp(Exception, "Feed dict asks for variable named 'non_existent' but no such variable is known to exist", do_fit)
def build_simple_model(self): """Build a simple model for test Returns: DNN, [ (input layer name, input placeholder, input data) ], Target data """ inputPlaceholder1, inputPlaceholder2 = \ tf.placeholder(tf.float32, (1, 1), name = "input1"), tf.placeholder(tf.float32, (1, 1), name = "input2") input1 = zqtflearn.input_data(placeholder=inputPlaceholder1) input2 = zqtflearn.input_data(placeholder=inputPlaceholder2) network = zqtflearn.merge([input1, input2], "sum") network = zqtflearn.reshape(network, (1, 1)) network = zqtflearn.fully_connected(network, 1) network = zqtflearn.regression(network) return ( zqtflearn.DNN(network), [("input1:0", inputPlaceholder1, self.INPUT_DATA_1), ("input2:0", inputPlaceholder2, self.INPUT_DATA_2)], self.TARGET, )
trainable_vars=disc_vars, batch_size=64, name='target_disc', op_name='DISC') gen_vars = zqtflearn.get_layer_variables_by_scope('Generator') gan_model = zqtflearn.regression(stacked_gan_net, optimizer='adam', loss='categorical_crossentropy', trainable_vars=gen_vars, batch_size=64, name='target_gen', op_name='GEN') # Define GAN model, that output the generated images. gan = zqtflearn.DNN(gan_model) # Training # Prepare input data to feed to the discriminator disc_noise = np.random.uniform(-1., 1., size=[total_samples, z_dim]) # Prepare target data to feed to the discriminator (0: fake image, 1: real image) y_disc_fake = np.zeros(shape=[total_samples]) y_disc_real = np.ones(shape=[total_samples]) y_disc_fake = zqtflearn.data_utils.to_categorical(y_disc_fake, 2) y_disc_real = zqtflearn.data_utils.to_categorical(y_disc_real, 2) # Prepare input data to feed to the stacked generator/discriminator gen_noise = np.random.uniform(-1., 1., size=[total_samples, z_dim]) # Prepare target data to feed to the discriminator # Generator tries to fool the discriminator, thus target is 1 (e.g. real images) y_gen = np.ones(shape=[total_samples])
X_test = h5f['cifar10_X_test'] Y_test = h5f['cifar10_Y_test'] # Build network network = input_data(shape=[None, 32, 32, 3], dtype=tf.float32) network = conv_2d(network, 32, 3, activation='relu') network = max_pool_2d(network, 2) network = conv_2d(network, 64, 3, activation='relu') network = conv_2d(network, 64, 3, activation='relu') network = max_pool_2d(network, 2) network = fully_connected(network, 512, activation='relu') network = dropout(network, 0.5) network = fully_connected(network, 10, activation='softmax') network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) # Training model = zqtflearn.DNN(network, tensorboard_verbose=0) model.fit(X, Y, n_epoch=50, shuffle=True, validation_set=(X_test, Y_test), show_metric=True, batch_size=96, run_id='cifar10_cnn') h5f.close()
def build_model(self, learning_rate=[0.001, 0.01]): ''' Model - wide and deep - built using zqtflearn ''' n_cc = len(self.continuous_columns) n_cc = 108 input_shape = [None, n_cc] if self.verbose: print("=" * 77 + " Model %s (type=%s)" % (self.name, self.model_type)) print(" Input placeholder shape=%s" % str(input_shape)) wide_inputs = zqtflearn.input_data(shape=input_shape, name="wide_X") deep_inputs = zqtflearn.input_data(shape=[None, 1], name="deep_X") if not isinstance(learning_rate, list): learning_rate = [learning_rate, learning_rate] # wide, deep if self.verbose: print(" Learning rates (wide, deep)=%s" % learning_rate) with tf.name_scope( "Y"): # placeholder for target variable (i.e. trainY input) Y_in = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="Y") with tf.variable_op_scope([wide_inputs], None, "cb_unit", reuse=False) as scope: central_bias = zqtflearn.variables.variable( 'central_bias', shape=[1], initializer=tf.constant_initializer(np.random.randn()), trainable=True, restore=True) tf.add_to_collection(tf.GraphKeys.LAYER_VARIABLES + '/cb_unit', central_bias) if 'wide' in self.model_type: wide_network = self.wide_model(wide_inputs, n_cc) network = wide_network wide_network_with_bias = tf.add(wide_network, central_bias, name="wide_with_bias") if 'deep' in self.model_type: deep_network = self.deep_model( wide_inputs, deep_inputs, n_cc ) # 这里面是wide inputs,在这个函数内部wide_inputs,会和deep_model制造的输入相合并。 deep_network_with_bias = tf.add(deep_network, central_bias, name="deep_with_bias") if 'wide' in self.model_type: network = tf.add(wide_network, deep_network) if self.verbose: print("Wide + deep model network %s" % network) else: network = deep_network network = tf.add(network, central_bias, name="add_central_bias") # add validation monitor summaries giving confusion matrix entries with tf.name_scope('Monitors'): predictions = tf.cast(tf.greater(network, 0), tf.int64) print("predictions=%s" % predictions) Ybool = tf.cast(Y_in, tf.bool) print("Ybool=%s" % Ybool) pos = tf.boolean_mask(predictions, Ybool) neg = tf.boolean_mask(predictions, ~Ybool) psize = tf.cast(tf.shape(pos)[0], tf.int64) nsize = tf.cast(tf.shape(neg)[0], tf.int64) true_positive = tf.reduce_sum(pos, name="true_positive") false_negative = tf.subtract(psize, true_positive, name="false_negative") false_positive = tf.reduce_sum(neg, name="false_positive") true_negative = tf.subtract(nsize, false_positive, name="true_negative") overall_accuracy = tf.truediv(tf.add(true_positive, true_negative), tf.add(nsize, psize), name="overall_accuracy") vmset = [ true_positive, true_negative, false_positive, false_negative, overall_accuracy ] trainable_vars = tf.trainable_variables() tv_deep = [v for v in trainable_vars if v.name.startswith('deep_')] tv_wide = [v for v in trainable_vars if v.name.startswith('wide_')] if self.verbose: print("DEEP trainable_vars") for v in tv_deep: print(" Variable %s: %s" % (v.name, v)) print("WIDE trainable_vars") for v in tv_wide: print(" Variable %s: %s" % (v.name, v)) # if 'wide' in self.model_type: # if not 'deep' in self.model_type: # tv_wide.append(central_bias) # zqtflearn.regression(wide_network_with_bias, # placeholder=Y_in, # optimizer='sgd', # loss='roc_auc_score', # #loss='binary_crossentropy', # metric="accuracy", # learning_rate=learning_rate[0], # validation_monitors=vmset, # trainable_vars=tv_wide, # op_name="wide_regression", # name="Y") # # if 'deep' in self.model_type: # if not 'wide' in self.model_type: # tv_wide.append(central_bias) # zqtflearn.regression(deep_network_with_bias, # placeholder=Y_in, # optimizer='adam', # loss='roc_auc_score', # #loss='binary_crossentropy', # metric="accuracy", # learning_rate=learning_rate[1], # validation_monitors=vmset if not 'wide' in self.model_type else None, # trainable_vars=tv_deep, # op_name="deep_regression", # name="Y") if self.model_type == 'wide+deep': # learn central bias separately for wide+deep zqtflearn.regression( network, placeholder=Y_in, optimizer='adam', #loss="roc_auc_score", loss='binary_crossentropy', metric="accuracy", validation_monitors=vmset, learning_rate=learning_rate[0], # use wide learning rate #trainable_vars=[central_bias], #[tv_deep,tv_wide,central_bias] # None op_name="central_bias_regression", name="Y") self.model = zqtflearn.DNN( network, tensorboard_verbose=self.tensorboard_verbose, max_checkpoints=self.max_checkpoints, checkpoint_path="%s/%s.tfl" % (self.checkpoints_dir, self.name), tensorboard_dir=self.tensorboard_dir) # tensorboard_dir="/tmp/tflearn_logs/" zqtflearn.DNN 我把他改为当前目录下的了,这样也比较好规范 if 'deep' in self.model_type: embeddingWeights = zqtflearn.get_layer_variables_by_name( 'deep_video_ids_embed')[0] # CUSTOM_WEIGHT = pickle.load("Haven't deal") # emb = np.array(CUSTOM_WEIGHT, dtype=np.float32) # emb = self.embedding new_emb_t = tf.convert_to_tensor(self.embedding) self.model.set_weights(embeddingWeights, new_emb_t) if self.verbose: print("Target variables:") for v in tf.get_collection(tf.GraphKeys.TARGETS): print(" variable %s: %s" % (v.name, v)) print("=" * 77) print("model build finish")
def build_model(self, learning_rate=[0.001, 0.01]): ''' Model - wide and deep - built using zqtflearn ''' n_cc = len(self.continuous_columns) n_categories = 1 # two categories: is_idv and is_not_idv input_shape = [None, n_cc] if self.verbose: print ("="*77 + " Model %s (type=%s)" % (self.name, self.model_type)) print (" Input placeholder shape=%s" % str(input_shape)) wide_inputs = zqtflearn.input_data(shape=input_shape, name="wide_X") if not isinstance(learning_rate, list): learning_rate = [learning_rate, learning_rate] # wide, deep if self.verbose: print (" Learning rates (wide, deep)=%s" % learning_rate) with tf.name_scope("Y"): # placeholder for target variable (i.e. trainY input) Y_in = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="Y") with tf.variable_scope(None, "cb_unit", [wide_inputs]) as scope: central_bias = zqtflearn.variables.variable('central_bias', shape=[1], initializer=tf.constant_initializer(np.random.randn()), trainable=True, restore=True) tf.add_to_collection(tf.GraphKeys.LAYER_VARIABLES + '/cb_unit', central_bias) if 'wide' in self.model_type: wide_network = self.wide_model(wide_inputs, n_cc) network = wide_network wide_network_with_bias = tf.add(wide_network, central_bias, name="wide_with_bias") if 'deep' in self.model_type: deep_network = self.deep_model(wide_inputs, n_cc) deep_network_with_bias = tf.add(deep_network, central_bias, name="deep_with_bias") if 'wide' in self.model_type: network = tf.add(wide_network, deep_network) if self.verbose: print ("Wide + deep model network %s" % network) else: network = deep_network network = tf.add(network, central_bias, name="add_central_bias") # add validation monitor summaries giving confusion matrix entries with tf.name_scope('Monitors'): predictions = tf.cast(tf.greater(network, 0), tf.int64) print ("predictions=%s" % predictions) Ybool = tf.cast(Y_in, tf.bool) print ("Ybool=%s" % Ybool) pos = tf.boolean_mask(predictions, Ybool) neg = tf.boolean_mask(predictions, ~Ybool) psize = tf.cast(tf.shape(pos)[0], tf.int64) nsize = tf.cast(tf.shape(neg)[0], tf.int64) true_positive = tf.reduce_sum(pos, name="true_positive") false_negative = tf.subtract(psize, true_positive, name="false_negative") false_positive = tf.reduce_sum(neg, name="false_positive") true_negative = tf.subtract(nsize, false_positive, name="true_negative") overall_accuracy = tf.truediv(tf.add(true_positive, true_negative), tf.add(nsize, psize), name="overall_accuracy") vmset = [true_positive, true_negative, false_positive, false_negative, overall_accuracy] trainable_vars = tf.trainable_variables() tv_deep = [v for v in trainable_vars if v.name.startswith('deep_')] tv_wide = [v for v in trainable_vars if v.name.startswith('wide_')] if self.verbose: print ("DEEP trainable_vars") for v in tv_deep: print (" Variable %s: %s" % (v.name, v)) print ("WIDE trainable_vars") for v in tv_wide: print (" Variable %s: %s" % (v.name, v)) if 'wide' in self.model_type: if not 'deep' in self.model_type: tv_wide.append(central_bias) zqtflearn.regression(wide_network_with_bias, placeholder=Y_in, optimizer='sgd', #loss='roc_auc_score', loss='binary_crossentropy', metric="accuracy", learning_rate=learning_rate[0], validation_monitors=vmset, trainable_vars=tv_wide, op_name="wide_regression", name="Y") if 'deep' in self.model_type: if not 'wide' in self.model_type: tv_wide.append(central_bias) zqtflearn.regression(deep_network_with_bias, placeholder=Y_in, optimizer='adam', #loss='roc_auc_score', loss='binary_crossentropy', metric="accuracy", learning_rate=learning_rate[1], validation_monitors=vmset if not 'wide' in self.model_type else None, trainable_vars=tv_deep, op_name="deep_regression", name="Y") if self.model_type=='wide+deep': # learn central bias separately for wide+deep zqtflearn.regression(network, placeholder=Y_in, optimizer='adam', loss='binary_crossentropy', metric="accuracy", learning_rate=learning_rate[0], # use wide learning rate trainable_vars=[central_bias], op_name="central_bias_regression", name="Y") self.model = zqtflearn.DNN(network, tensorboard_verbose=self.tensorboard_verbose, max_checkpoints=5, checkpoint_path="%s/%s.tfl" % (self.checkpoints_dir, self.name), ) if self.verbose: print ("Target variables:") for v in tf.get_collection(tf.GraphKeys.TARGETS): print (" variable %s: %s" % (v.name, v)) print ("="*77)
+ (1 - x_true) * tf.log(1e-10 + 1 - x_reconstructed) encode_decode_loss = -tf.reduce_sum(encode_decode_loss, 1) # KL Divergence loss kl_div_loss = 1 + z_std - tf.square(z_mean) - tf.exp(z_std) kl_div_loss = -0.5 * tf.reduce_sum(kl_div_loss, 1) return tf.reduce_mean(encode_decode_loss + kl_div_loss) net = zqtflearn.regression(decoder, optimizer='rmsprop', learning_rate=0.001, loss=vae_loss, metric=None, name='target_images') # We will need 2 models, one for training that will learn the latent # representation, and one that can take random normal noise as input and # use the decoder part of the network to generate an image # Train the VAE training_model = zqtflearn.DNN(net, tensorboard_verbose=0) training_model.fit({'input_images': X}, {'target_images': X}, n_epoch=100, validation_set=(testX, testX), batch_size=256, run_id="vae") # Build an image generator (re-using the decoding layers) # Input data is a normal (gaussian) random distribution (with dim = latent_dim) input_noise = zqtflearn.input_data(shape=[None, latent_dim], name='input_noise') decoder = zqtflearn.fully_connected(input_noise, hidden_dim, activation='relu', scope='decoder_h', reuse=True) decoder = zqtflearn.fully_connected(decoder, original_dim, activation='sigmoid', scope='decoder_out', reuse=True) generator_model = zqtflearn.DNN(decoder, session=training_model.session) # Building a manifold of generated digits n = 25 # Figure row size figure = np.zeros((28 * n, 28 * n))
activation='relu', name='block5_conv2') block5_conv3 = conv_2d(block5_conv2, 512, 3, activation='relu', name='block5_conv3') block5_conv4 = conv_2d(block5_conv3, 512, 3, activation='relu', name='block5_conv4') block4_pool = max_pool_2d(block5_conv4, 2, strides=2, name='block4_pool') flatten_layer = zqtflearn.layers.core.flatten(block4_pool, name='Flatten') fc1 = fully_connected(flatten_layer, 4096, activation='relu') dp1 = dropout(fc1, 0.5) fc2 = fully_connected(dp1, 4096, activation='relu') dp2 = dropout(fc2, 0.5) network = fully_connected(dp2, 1000, activation='rmsprop') regression = zqtflearn.regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) model = zqtflearn.DNN(regression, checkpoint_path='vgg19', tensorboard_dir="./logs")
def test_dnn_loading_scope(self): with tf.Graph().as_default(): X = [ 3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1 ] Y = [ 1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3 ] input = zqtflearn.input_data(shape=[None]) linear = zqtflearn.single_unit(input) regression = zqtflearn.regression(linear, optimizer='sgd', loss='mean_square', metric='R2', learning_rate=0.01) m = zqtflearn.DNN(regression) # Testing fit and predict m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False) res = m.predict([3.2])[0] self.assertGreater( res, 1.3, "DNN test (linear regression) failed! with score: " + str(res) + " expected > 1.3") self.assertLess( res, 1.8, "DNN test (linear regression) failed! with score: " + str(res) + " expected < 1.8") # Testing save method m.save("test_dnn.zqtflearn") self.assertTrue(os.path.exists("test_dnn.zqtflearn.index")) # Testing loading, with change of variable scope (saved with no scope, now loading into scopeA) with tf.Graph().as_default(): # start with clear graph with tf.variable_scope("scopeA") as scope: input = zqtflearn.input_data(shape=[None]) linear = zqtflearn.single_unit(input) regression = zqtflearn.regression(linear, optimizer='sgd', loss='mean_square', metric='R2', learning_rate=0.01) m = zqtflearn.DNN(regression) def try_load(): m.load("test_dnn.zqtflearn") self.assertRaises( tf.errors.NotFoundError, try_load) # fails, since names in file don't have "scopeA" m.load("test_dnn.zqtflearn", variable_name_map=( "scopeA/", "")) # succeeds, because variable names are rewritten res = m.predict([3.2])[0] self.assertGreater( res, 1.3, "DNN test (linear regression) failed after loading model! score: " + str(res) + " expected > 1.3") self.assertLess( res, 1.8, "DNN test (linear regression) failed after loading model! score: " + str(res) + " expected < 1.8")
# Building Residual Network net = zqtflearn.input_data(shape=[None, 28, 28, 1]) net = zqtflearn.conv_2d(net, 64, 3, activation='relu', bias=False) # Residual blocks net = zqtflearn.residual_bottleneck(net, 3, 16, 64) net = zqtflearn.residual_bottleneck(net, 1, 32, 128, downsample=True) net = zqtflearn.residual_bottleneck(net, 2, 32, 128) net = zqtflearn.residual_bottleneck(net, 1, 64, 256, downsample=True) net = zqtflearn.residual_bottleneck(net, 2, 64, 256) net = zqtflearn.batch_normalization(net) net = zqtflearn.activation(net, 'relu') net = zqtflearn.global_avg_pool(net) # Regression net = zqtflearn.fully_connected(net, 10, activation='softmax') net = zqtflearn.regression(net, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.1) # Training model = zqtflearn.DNN(net, checkpoint_path='model_resnet_mnist', max_checkpoints=10, tensorboard_verbose=0) model.fit(X, Y, n_epoch=100, validation_set=(testX, testY), show_metric=True, batch_size=256, run_id='resnet_mnist')
# categorical_labels=True, normalize=True, # files_extension=['.jpg', '.png'], filter_channel=True) num_classes = 10 # num of your dataset # VGG preprocessing img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center(mean=[123.68, 116.779, 103.939], per_channel=True) # VGG Network x = zqtflearn.input_data(shape=[None, 224, 224, 3], name='input', data_preprocessing=img_prep) softmax = vgg16(x, num_classes) regression = zqtflearn.regression(softmax, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001, restore=False) model = zqtflearn.DNN(regression, checkpoint_path='vgg-finetuning', max_checkpoints=3, tensorboard_verbose=2, tensorboard_dir="./logs") model_file = os.path.join(model_path, "vgg16.zqtflearn") model.load(model_file, weights_only=True) # Start finetuning model.fit(X, Y, n_epoch=10, validation_set=0.1, shuffle=True, show_metric=True, batch_size=64, snapshot_epoch=False, snapshot_step=200, run_id='vgg-finetuning') model.save('your-task-model-retrained-by-vgg')
encoder = zqtflearn.fully_connected(encoder, 256) encoder = zqtflearn.fully_connected(encoder, 64) # Building the decoder decoder = zqtflearn.fully_connected(encoder, 256) decoder = zqtflearn.fully_connected(decoder, 784, activation='sigmoid') # Regression, with mean square error net = zqtflearn.regression(decoder, optimizer='adam', learning_rate=0.001, loss='mean_square', metric=None) # Training the auto encoder model = zqtflearn.DNN(net, tensorboard_verbose=0) model.fit(X, X, n_epoch=20, validation_set=(testX, testX), run_id="auto_encoder", batch_size=256) # Encoding X[0] for test print("\nTest encoding of X[0]:") # New model, re-using the same session, for weights sharing encoding_model = zqtflearn.DNN(encoder, session=model.session) print(encoding_model.predict([X[0]])) # Testing the image reconstruction on new data (test set) print("\nVisualizing results after being encoded and decoded:")
activation=None, name='Conv2d_7b_1x1'))) net = avg_pool_2d(net, net.get_shape().as_list()[1:3], strides=2, padding='VALID', name='AvgPool_1a_8x8') net = flatten(net) net = dropout(net, dropout_keep_prob) loss = fully_connected(net, num_classes, activation='softmax') network = zqtflearn.regression(loss, optimizer='RMSprop', loss='categorical_crossentropy', learning_rate=0.0001) model = zqtflearn.DNN(network, checkpoint_path='inception_resnet_v2', max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir="./tflearn_logs/") model.fit(X, Y, n_epoch=1000, validation_set=0.1, shuffle=True, show_metric=True, batch_size=32, snapshot_step=2000, snapshot_epoch=False, run_id='inception_resnet_v2_17flowers')
network = conv_2d(network, 512, 3, activation='relu') network = conv_2d(network, 512, 3, activation='relu') network = conv_2d(network, 512, 3, activation='relu') network = max_pool_2d(network, 2, strides=2) network = fully_connected(network, 4096, activation='relu') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='relu') network = dropout(network, 0.5) network = fully_connected(network, 17, activation='softmax') network = regression(network, optimizer='rmsprop', loss='categorical_crossentropy', learning_rate=0.0001) # Training model = zqtflearn.DNN(network, checkpoint_path='model_vgg', max_checkpoints=1, tensorboard_verbose=0) model.fit(X, Y, n_epoch=500, shuffle=True, show_metric=True, batch_size=32, snapshot_step=500, snapshot_epoch=False, run_id='vgg_oxflowers17')
# MNIST Data X, Y, testX, testY = mnist.load_data(one_hot=True) # Model input_layer = zqtflearn.input_data(shape=[None, 784], name='input') dense1 = zqtflearn.fully_connected(input_layer, 128, name='dense1') dense2 = zqtflearn.fully_connected(dense1, 256, name='dense2') softmax = zqtflearn.fully_connected(dense2, 10, activation='softmax') regression = zqtflearn.regression(softmax, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy') # Define classifier, with model checkpoint (autosave) model = zqtflearn.DNN(regression, checkpoint_path='model.tfl.ckpt') # Train model, with model checkpoint every epoch and every 200 training steps. model.fit( X, Y, n_epoch=1, validation_set=(testX, testY), show_metric=True, snapshot_epoch=True, # Snapshot (save & evaluate) model every epoch. snapshot_step=500, # Snapshot (save & evalaute) model every 500 steps. run_id='model_and_weights') # --------------------- # Save and load a model # ---------------------
passengers[i][1] = 1. if passengers[i][1] == 'female' else 0. return np.array(passengers, dtype=np.float32) # Ignore 'name' and 'ticket' columns (id 1 & 6 of data array) to_ignore=[1, 6] # Preprocess data data = preprocess(data, to_ignore) # Build neural network net = zqtflearn.input_data(shape=[None, 6]) net = zqtflearn.fully_connected(net, 32) net = zqtflearn.fully_connected(net, 32) net = zqtflearn.fully_connected(net, 2, activation='softmax') net = zqtflearn.regression(net) # Define model model = zqtflearn.DNN(net) # Start training (apply gradient descent algorithm) model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True) # Let's create some data for DiCaprio and Winslet dicaprio = [3, 'Jack Dawson', 'male', 19, 0, 0, 'N/A', 5.0000] winslet = [1, 'Rose DeWitt Bukater', 'female', 17, 1, 2, 'N/A', 100.0000] # Preprocess data dicaprio, winslet = preprocess([dicaprio, winslet], to_ignore) # Predict surviving chances (class 1 results) pred = model.predict([dicaprio, winslet]) print("DiCaprio Surviving Rate:", pred[0][1]) print("Winslet Surviving Rate:", pred[1][1])
def build_model(self, learning_rate=[0.001, 0.01]): ''' Model - wide and deep - built using tflearn ''' n_cc = len(self.continuous_columns) n_cc = 108 input_shape = [None, n_cc] if self.verbose: print("=" * 77 + " Model %s (type=%s)" % (self.name, self.model_type)) print(" Input placeholder shape=%s" % str(input_shape)) wide_inputs = zqtflearn.input_data(shape=input_shape, name="wide_X") if not isinstance(learning_rate, list): learning_rate = [learning_rate, learning_rate] # wide, deep if self.verbose: print(" Learning rates (wide, deep)=%s" % learning_rate) with tf.name_scope( "Y"): # placeholder for target variable (i.e. trainY input) Y_in = tf.placeholder(shape=[None, 1], dtype=tf.float32, name="Y") with tf.variable_op_scope([wide_inputs], None, "cb_unit", reuse=False) as scope: central_bias = zqtflearn.variables.variable( 'central_bias', shape=[1], initializer=tf.constant_initializer(np.random.randn()), trainable=True, restore=True) tf.add_to_collection(tf.GraphKeys.LAYER_VARIABLES + '/cb_unit', central_bias) wide_network = self.wide_model(wide_inputs, n_cc) network = wide_network network = tf.add(network, central_bias, name="add_central_bias") # add validation monitor summaries giving confusion matrix entries with tf.name_scope('Monitors'): predictions = tf.cast(tf.greater(network, 0), tf.int64) print("predictions=%s" % predictions) Ybool = tf.cast(Y_in, tf.bool) print("Ybool=%s" % Ybool) pos = tf.boolean_mask(predictions, Ybool) neg = tf.boolean_mask(predictions, ~Ybool) psize = tf.cast(tf.shape(pos)[0], tf.int64) nsize = tf.cast(tf.shape(neg)[0], tf.int64) true_positive = tf.reduce_sum(pos, name="true_positive") false_negative = tf.subtract(psize, true_positive, name="false_negative") false_positive = tf.reduce_sum(neg, name="false_positive") true_negative = tf.subtract(nsize, false_positive, name="true_negative") overall_accuracy = tf.truediv(tf.add(true_positive, true_negative), tf.add(nsize, psize), name="overall_accuracy") vmset = [ true_positive, true_negative, false_positive, false_negative, overall_accuracy ] zqtflearn.regression( network, placeholder=Y_in, optimizer='adam', #loss="roc_auc_score", loss='binary_crossentropy', metric="accuracy", learning_rate=learning_rate[0], # use wide learning rate # trainable_vars=[central_bias], validation_monitors=vmset, op_name="central_bias_regression", name="Y") self.model = zqtflearn.DNN( network, tensorboard_verbose=self.tensorboard_verbose, max_checkpoints=self.max_checkpoints, checkpoint_path="%s/%s.tfl" % (self.checkpoints_dir, self.name), tensorboard_dir=self.tensorboard_dir) if self.verbose: print("Target variables:") for v in tf.get_collection(tf.GraphKeys.TARGETS): print(" variable %s: %s" % (v.name, v)) print("=" * 77)
import zqtflearn # Regression data X = [ 3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1 ] Y = [ 1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3 ] # Linear Regression graph input_ = zqtflearn.input_data(shape=[None]) linear = zqtflearn.single_unit(input_) regression = zqtflearn.regression(linear, optimizer='sgd', loss='mean_square', metric='R2', learning_rate=0.01) m = zqtflearn.DNN(regression) m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False) print("\nRegression result:") print("Y = " + str(m.get_weights(linear.W)) + "*X + " + str(m.get_weights(linear.b))) print("\nTest prediction for x = 3.2, 3.3, 3.4:") print(m.predict([3.2, 3.3, 3.4])) # should output (close, not exact) y = [1.5315033197402954, 1.5585315227508545, 1.5855598449707031]
""" from __future__ import absolute_import, division, print_function import zqtflearn import numpy as np # Regression data- 10 training instances #10 input features per instance. X=np.random.rand(10,10).tolist() #2 output features per instance Y=np.random.rand(10,2).tolist() # Multiple Regression graph, 10-d input layer input_ = zqtflearn.input_data(shape=[None, 10]) #10-d fully connected layer r1 = zqtflearn.fully_connected(input_, 10) #2-d fully connected layer for output r1 = zqtflearn.fully_connected(r1, 2) r1 = zqtflearn.regression(r1, optimizer='sgd', loss='mean_square', metric='R2', learning_rate=0.01) m = zqtflearn.DNN(r1) m.fit(X,Y, n_epoch=100, show_metric=True, snapshot_epoch=False) #Predict for 1 instance testinstance=np.random.rand(1,10).tolist() print("\nInput features: ",testinstance) print("\n Predicted output: ") print(m.predict(testinstance))
X = [[0.], [1.]] Y = [[1.], [0.]] # Graph definition with tf.Graph().as_default(): g = zqtflearn.input_data(shape=[None, 1]) g = zqtflearn.fully_connected(g, 128, activation='linear') g = zqtflearn.fully_connected(g, 128, activation='linear') g = zqtflearn.fully_connected(g, 1, activation='sigmoid') g = zqtflearn.regression(g, optimizer='sgd', learning_rate=2., loss='mean_square') # Model training m = zqtflearn.DNN(g) m.fit(X, Y, n_epoch=100, snapshot_epoch=False) # Test model print("Testing NOT operator") print("NOT 0:", m.predict([[0.]])) print("NOT 1:", m.predict([[1.]])) # Logical OR operator X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]] Y = [[0.], [1.], [1.], [1.]] # Graph definition with tf.Graph().as_default(): g = zqtflearn.input_data(shape=[None, 2]) g = zqtflearn.fully_connected(g, 128, activation='linear')
def test_vm1(self): with tf.Graph().as_default(): # Data loading and preprocessing import zqtflearn.datasets.mnist as mnist X, Y, testX, testY = mnist.load_data(one_hot=True) X = X.reshape([-1, 28, 28, 1]) testX = testX.reshape([-1, 28, 28, 1]) X = X[:10, :, :, :] Y = Y[:10, :] # Building convolutional network network = input_data(shape=[None, 28, 28, 1], name='input') network = conv_2d(network, 32, 3, activation='relu', regularizer="L2") network = max_pool_2d(network, 2) network = local_response_normalization(network) network = conv_2d(network, 64, 3, activation='relu', regularizer="L2") network = max_pool_2d(network, 2) network = local_response_normalization(network) network = fully_connected(network, 128, activation='tanh') network = dropout(network, 0.8) network = fully_connected(network, 256, activation='tanh') network = dropout(network, 0.8) # construct two varaibles to add as additional "valiation monitors" # these varaibles are evaluated each time validation happens (eg at a snapshot) # and the results are summarized and output to the tensorboard events file, # together with the accuracy and loss plots. # # Here, we generate a dummy variable given by the sum over the current # network tensor, and a constant variable. In practice, the validation # monitor may present useful information, like confusion matrix # entries, or an AUC metric. with tf.name_scope('CustomMonitor'): test_var = tf.reduce_sum(tf.cast(network, tf.float32), name="test_var") test_const = tf.constant(32.0, name="custom_constant") print("network=%s, test_var=%s" % (network, test_var)) network = fully_connected(network, 10, activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.01, loss='categorical_crossentropy', name='target', validation_monitors=[test_var, test_const]) # Training model = zqtflearn.DNN(network, tensorboard_verbose=3) model.fit({'input': X}, {'target': Y}, n_epoch=1, validation_set=({ 'input': testX }, { 'target': testY }), snapshot_step=10, show_metric=True, run_id='convnet_mnist') # check for validation monitor variables ats = tf.get_collection("Adam_testing_summaries") print("ats=%s" % ats) self.assertTrue( len(ats) == 4 ) # should be four variables being summarized: [loss, test_var, test_const, accuracy] session = model.session print("session=%s" % session) trainer = model.trainer print("train_ops = %s" % trainer.train_ops) top = trainer.train_ops[0] vmtset = top.validation_monitors_T print("validation_monitors_T = %s" % vmtset) with model.session.as_default(): ats_var_val = zqtflearn.variables.get_value(vmtset[0]) ats_const_val = zqtflearn.variables.get_value(vmtset[1]) print("summary values: var=%s, const=%s" % (ats_var_val, ats_const_val)) self.assertTrue( ats_const_val == 32) # test to make sure the constant made it through
from zqtflearn.layers.embedding_ops import embedding from zqtflearn.layers.recurrent import bidirectional_rnn, BasicLSTMCell from zqtflearn.layers.estimator import regression # IMDB Dataset loading train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000, valid_portion=0.1) trainX, trainY = train testX, testY = test # Data preprocessing # Sequence padding trainX = pad_sequences(trainX, maxlen=200, value=0.) testX = pad_sequences(testX, maxlen=200, value=0.) # Converting labels to binary vectors trainY = to_categorical(trainY) testY = to_categorical(testY) # Network building net = input_data(shape=[None, 200]) net = embedding(net, input_dim=20000, output_dim=128) net = bidirectional_rnn(net, BasicLSTMCell(128), BasicLSTMCell(128)) net = dropout(net, 0.5) net = fully_connected(net, 2, activation='softmax') net = regression(net, optimizer='adam', loss='categorical_crossentropy') # Training model = zqtflearn.DNN(net, clip_gradients=0., tensorboard_verbose=2) model.fit(trainX, trainY, validation_set=0.1, show_metric=True, batch_size=64)